{"id":3293808,"url":"https://alion.io/job/terra-technology-circle-consulting-data-scientist","title":"Data Scientist","company":{"id":3812592,"name":"Terra Technology Circle Consulting","domain":"terratcc.com","url":"https://alion.io/company/terra-technology-circle-consulting","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":13000,"max_usd":35000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":21},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"AI Agents","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"Kubernetes","optional":true},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-10-26T19:39:50Z","employer_posted_date":null,"last_verified_at":"2026-10-26T19:39:50Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Location\nThe successful candidate shall be placed at Pune Location. It is a Full-time Job, No remote work. Data Scientist (GenAI/ML,Python, FastAPI/Flask) willing to work on a 6 to12-months contract may apply.\n\nExperience\nCandidates should have experience between 3-6 years\n\nRole Description\n\nAbout the Role\nWe are looking for a skilled and motivated Data Scientist with strong experience in Python, Machine Learning, and Generative AI, along with hands-on exposure to Flask and FastAPI for building and deploying scalable data-driven applications. The ideal candidate will work closely with cross-functional teams to design, develop, and deploy intelligent solutions that drive business value.\n\nKey Responsibilities\nDesign, develop, and deploy machine learning and generative AI models for real-world business problems.\nBuild and expose ML/AI models using RESTful APIs with Flask and FastAPI.\nPerform data analysis, feature engineering, model training, validation, and optimization.\nWork on end-to-end ML pipelines, from data ingestion to model deployment and monitoring.\nCollaborate with product managers, engineers, and stakeholders to translate business requirements into technical solutions.\nOptimize model performance, scalability, and reliability in production environments.\nStay updated with the latest advancements in ML, Generative AI, and AI frameworks.\nDocument models, APIs, workflows, and best practices.\n\nRequired Skills & Qualifications\n3–6 years of hands-on experience as a Data Scientist or similar role.\nStrong proficiency in Python.\nSolid experience with Machine Learning algorithms (supervised, unsupervised, and deep learning).\nPractical exposure to Generative AI (LLMs, embeddings, prompt engineering, or fine-tuning).\nExperience building APIs using Flask and/or FastAPI.\nStrong understanding of data preprocessing, feature engineering, and model evaluation techniques.\nExperience with common ML libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, etc.).\nGood understanding of software engineering best practices and version control (Git).\n\nNice to Have\nExperience with cloud platforms (AWS, Azure, or GCP).\nKnowledge of MLOps tools and CI/CD pipelines.\nExposure to vector databases, RAG architectures, or AI agents.\nFamiliarity with Docker/Kubernetes.\nStrong communication and problem-solving 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